000			02633nam a2200349 i 4500
001			CR9781108966559
003			UkCbUP
005			20240730160753.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			200722s2022||||enk o ||1 0|eng|d
020			_a9781108966559 (ebook)
020			_z9781108832984 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aTK5103.2 _b.M3156 2022
082	0	0	_a621.382 _223/eng/20220318
245	0	0	_aMachine learning and wireless communications / _cedited by Yonina C. Eldar, Weizmann Institute of Science, Andrea Goldsmith, Princeton University, Deniz Gündüz, Imperial Colleg, H. Vincent Poor, Princeton University.
264		1	_aCambridge, United Kingdom ; New York, NY : _bCambridge University Press, _c2022.
300			_a1 online resource (xiv, 544 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 20 Jun 2022).
505	2		_aDeep neural networks for joint source-channel coding / David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Gündüz, Andrea Goldsmith -- Timely wireless edge inference / Sheng Zhou, Wenqi Shi, Xiufeng Huang, and Zhisheng Niu.
520			_aHow can machine learning help the design of future communication networks - and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications - an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.
650		0	_aWireless communication systems. _93474
650		0	_aMachine learning. _91831
700	1		_aEldar, Yonina C., _eeditor. _974598
776	0	8	_iPrint version: _z9781108832984
856	4	0	_uhttps://doi.org/10.1017/9781108966559
942			_cEBK
999			_c84175 _d84175